Abstract
Spatiotemporal prediction on climate data is aiming to predict future spatial data by learning from prior spatial sequence data. In this paper, we are interested in a prediction of upper tropospheric circulations over the Northern Hemisphere by predicting a geopotential height at 300 hPa (Z300) variable. We proposed a predictive model by constructing an architecture with convolutional layers and deconvolutional layers and applied to convolutional long short-term memory (ConvLSTM) network. The results show that our model obtained root mean square error (RMSE) of 77.36 m (0.84% comparing to average Z300 value) in short-term prediction. While, a convolutional neural network (CNN) and a linear regression (LR) model obtained RMSE of 109.35 (1.19%) and 153.61 (1.67%), respectively. The ConvLSTM maintains RMSE even in long-term prediction. Furthermore, the prediction features’ investigation result shows that temperature at 300 hPa (T300) and self prior Z300 features are important for Z300 prediction.
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Acknowledgment
This work was supported in part by the Network Joint Research Center for Materials and Devices and by JSPS KAKENHI Grant Number 19K22876.
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Phermphoonphiphat, E., Tomita, T., Numao, M., Fukui, Ki. (2020). A Study of Upper Tropospheric Circulations over the Northern Hemisphere Prediction Using Multivariate Features by ConvLSTM. In: Sato, H., Iwanaga, S., Ishii, A. (eds) Proceedings of the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems. IES 2019. Proceedings in Adaptation, Learning and Optimization, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-37442-6_12
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